Abstract
In this paper we determine the asymptotically efficient change of intensity for some problems of Monte Carlo simulation involving a finite state continuous time Markov process. Firstly, the computation of probabilities of large deviations of empirical averages from their asymptotic mean; second, the computation of probabilities of crossing a large level for the corresponding additive process. We are motivated by the study of overflows in a buffer whose input is modeled as a Markov fluid.
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Baldi, P., Piccioni, M. Importance Sampling for Continuous Time Markov Chains and Applications to Fluid Models*. Methodology and Computing in Applied Probability 1, 375–390 (1999). https://doi.org/10.1023/A:1010050800089
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DOI: https://doi.org/10.1023/A:1010050800089